A study on the man-hour prediction system for shipbuilding
- 616 Downloads
In shipbuilding, the man-hour is a unit widely used for production planning, with systematic prediction of man-hour taking greater importance in cost reduction. However, as the man-hours are predicted by experts at shipyards, existing methods have often resulted in incorrect predictions and cost significant amount of time. There have been several attempts made by many researchers to overcome such problems resulting from prediction by experts. Yet, their approaches considered only a limited number of factors such as ship specifications, and were not highly applicable at shipyards. In this study, we propose a system that predicts man-hours with deployable data in different times of manufacturing process and that can be applied in practical shipbuilding. The results demonstrated the possibility that our prediction system could be a good alternative to existing prediction methods.
KeywordsMan-hour prediction Shipbuilding Predictive models
This work (Grants No. 0420-20120062) was supported by Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2012. This work was supported by the Brain Korea 21 PLUS Project in 2013, the National Research Foundation (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030814). This work was also supported by the Engineering Research Institute of SNU.
- Ayoubloo, M. K., Azamathulla, H. M., Jabbari, E., & Zanganeh, M. (2011). Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART, ANN and linear regression approaches. Expert Systems with Applications, 38(8), 10114–10123.CrossRefGoogle Scholar
- Breiman, L. (1993). Classification and regression trees. RL: CRC Press.Google Scholar
- Carreyette, J. (1978). Preliminary ship cost estimation. Naval Architect (4).Google Scholar
- Chen, Y. S., Cheng, C. H., & Lai, C. J. (2010). A hybrid procedure for extracting rules of production performance in the automobile parts industry. Journal of Intelligent Manufacturing, 21(4), 423–437.Google Scholar
- Chien, C. F., Zheng, J. N., Lin, Y. J. (2013). Determining the operator-machine assignment for machine interference problem and an empirical study in semiconductor test facility. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0777-3.
- Gullander P, Davidsson A, Dencker K, Fasth As, Fässberg T, Harlin U, Stahre J (2011) Towards a production complexity model that supports operation, re-balancing and man-hour planning. In: Proceedings of the 4th Swedish Production Symposium (SPS), Lund, Sweden.Google Scholar
- Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. Journal of Machine Learning Research-Proceedings Track, 4, 60–73.Google Scholar
- Lee, J. K., Lee, K. J., Hong, J. S., Kim, W., Kim, E. Y., Choi, S. Y., et al. (1995). Das: Intelligent scheduling systems for shipbuilding. AI Magazine, 16(4), 78.Google Scholar
- Liu, B., & Zh, J. I. A. N. G. (2005). The intelligent man-hour estimate technique of assembly for shipbuilding. Journal of Shanghai Jiaotong University, 12, 1979–1983.Google Scholar
- Liu, B., & Jiang, Z. H. (2005). The man-hour estimation models & its comparison of interim products assembly for shipbuilding. International Journal of Operations Research, 2(1), 14–19.Google Scholar
- Liu, S., & Chen, J. (2007). Study on man-hour ration calculation with artificial neural networks. Machine Tool & Hydraulics, 1, 25.Google Scholar
- Rashwan, A. M. (2005). Estimation of Ship production man-hour. Alexandria Engineering Journal, 44(4), 527–533.Google Scholar
- Sandri, M., & Zuccolotto, P. (2008). A bias correction algorithm for the Gini variable importance measure in classification trees. Journal of Computational and Graphical Statistics, 17(3), 1–18. Google Scholar
- Shmueli, G., Patel, N. R., & Bruce, P. C. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. NY: Wiley.Google Scholar
- Sierra, M. R., Mencía, C., & Varela, R. (2013). New schedule generation schemes for the job-shop problem with operators. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0810-6.
- Zhong, R. Y., Huang , G. Q., Dai, Q. Y., & Zhang, T. (2012), Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-012-0721-y.